Other search tools

About this data

The publication data currently available has been vetted by Vanderbilt faculty, staff, administrators and trainees. The data itself is retrieved directly from NCBI's PubMed and is automatically updated on a weekly basis to ensure accuracy and completeness.

If you have any questions or comments, please contact us.

Results: 1 to 10 of 931

Publication Record

Connections

Multi-tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits.
Gamazon ER, Zwinderman AH, Cox NJ, Denys D, Derks EM
(2019) Nat Genet 51: 933-940
MeSH Terms: Algorithms, Computational Biology, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Genetic Association Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Mental Disorders, Organ Specificity, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Quantitative Trait, Heritable, Transcriptome
Show Abstract · Added July 17, 2019
The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression. We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (n associations = 499; n unique genes = 275), bipolar disorder (n associations = 17; n unique genes = 13), attention deficit hyperactivity disorder (n associations = 19; n unique genes = 12) and broad depression (n associations = 41; n unique genes = 31). Importantly, both PrediXcan and summary-data-based Mendelian randomization/heterogeneity in dependent instruments analyses suggest potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues.
0 Communities
1 Members
0 Resources
MeSH Terms
Gene expression imputation across multiple brain regions provides insights into schizophrenia risk.
Huckins LM, Dobbyn A, Ruderfer DM, Hoffman G, Wang W, Pardiñas AF, Rajagopal VM, Als TD, T Nguyen H, Girdhar K, Boocock J, Roussos P, Fromer M, Kramer R, Domenici E, Gamazon ER, Purcell S, CommonMind Consortium, Schizophrenia Working Group of the Psychiatric Genomics Consortium, iPSYCH-GEMS Schizophrenia Working Group, Demontis D, Børglum AD, Walters JTR, O'Donovan MC, Sullivan P, Owen MJ, Devlin B, Sieberts SK, Cox NJ, Im HK, Sklar P, Stahl EA
(2019) Nat Genet 51: 659-674
MeSH Terms: Brain, Case-Control Studies, Gene Expression, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Risk, Schizophrenia, Transcriptome
Show Abstract · Added July 17, 2019
Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.
0 Communities
1 Members
0 Resources
MeSH Terms
Functionally oriented analysis of cardiometabolic traits in a trans-ethnic sample.
Petty LE, Highland HM, Gamazon ER, Hu H, Karhade M, Chen HH, de Vries PS, Grove ML, Aguilar D, Bell GI, Huff CD, Hanis CL, Doddapaneni H, Munzy DM, Gibbs RA, Ma J, Parra EJ, Cruz M, Valladares-Salgado A, Arking DE, Barbeira A, Im HK, Morrison AC, Boerwinkle E, Below JE
(2019) Hum Mol Genet 28: 1212-1224
MeSH Terms: Adult, Aged, Blood Pressure, Body Mass Index, Chromosome Mapping, Ethnic Groups, European Continental Ancestry Group, Female, Forecasting, Genetic Association Studies, Genome-Wide Association Study, Humans, Male, Metabolome, Middle Aged, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Transcriptome
Show Abstract · Added February 15, 2019
Interpretation of genetic association results is difficult because signals often lack biological context. To generate hypotheses of the functional genetic etiology of complex cardiometabolic traits, we estimated the genetically determined component of gene expression from common variants using PrediXcan (1) and determined genes with differential predicted expression by trait. PrediXcan imputes tissue-specific expression levels from genetic variation using variant-level effect on gene expression in transcriptome data. To explore the value of imputed genetically regulated gene expression (GReX) models across different ancestral populations, we evaluated imputed expression levels for predictive accuracy genome-wide in RNA sequence data in samples drawn from European-ancestry and African-ancestry populations and identified substantial predictive power using European-derived models in a non-European target population. We then tested the association of GReX on 15 cardiometabolic traits including blood lipid levels, body mass index, height, blood pressure, fasting glucose and insulin, RR interval, fibrinogen level, factor VII level and white blood cell and platelet counts in 15 755 individuals across three ancestry groups, resulting in 20 novel gene-phenotype associations reaching experiment-wide significance across ancestries. In addition, we identified 18 significant novel gene-phenotype associations in our ancestry-specific analyses. Top associations were assessed for additional support via query of S-PrediXcan (2) results derived from publicly available genome-wide association studies summary data. Collectively, these findings illustrate the utility of transcriptome-based imputation models for discovery of cardiometabolic effect genes in a diverse dataset.
© The Author(s) 2019. Published by Oxford University Press.
0 Communities
1 Members
0 Resources
19 MeSH Terms
Trans-ethnic association study of blood pressure determinants in over 750,000 individuals.
Giri A, Hellwege JN, Keaton JM, Park J, Qiu C, Warren HR, Torstenson ES, Kovesdy CP, Sun YV, Wilson OD, Robinson-Cohen C, Roumie CL, Chung CP, Birdwell KA, Damrauer SM, DuVall SL, Klarin D, Cho K, Wang Y, Evangelou E, Cabrera CP, Wain LV, Shrestha R, Mautz BS, Akwo EA, Sargurupremraj M, Debette S, Boehnke M, Scott LJ, Luan J, Zhao JH, Willems SM, Thériault S, Shah N, Oldmeadow C, Almgren P, Li-Gao R, Verweij N, Boutin TS, Mangino M, Ntalla I, Feofanova E, Surendran P, Cook JP, Karthikeyan S, Lahrouchi N, Liu C, Sepúlveda N, Richardson TG, Kraja A, Amouyel P, Farrall M, Poulter NR, Understanding Society Scientific Group, International Consortium for Blood Pressure, Blood Pressure-International Consortium of Exome Chip Studies, Laakso M, Zeggini E, Sever P, Scott RA, Langenberg C, Wareham NJ, Conen D, Palmer CNA, Attia J, Chasman DI, Ridker PM, Melander O, Mook-Kanamori DO, Harst PV, Cucca F, Schlessinger D, Hayward C, Spector TD, Jarvelin MR, Hennig BJ, Timpson NJ, Wei WQ, Smith JC, Xu Y, Matheny ME, Siew EE, Lindgren C, Herzig KH, Dedoussis G, Denny JC, Psaty BM, Howson JMM, Munroe PB, Newton-Cheh C, Caulfield MJ, Elliott P, Gaziano JM, Concato J, Wilson PWF, Tsao PS, Velez Edwards DR, Susztak K, Million Veteran Program, O'Donnell CJ, Hung AM, Edwards TL
(2019) Nat Genet 51: 51-62
MeSH Terms: Adolescent, Animals, Blood Pressure, Ethnic Groups, Female, Gene Expression, Genome-Wide Association Study, Humans, Kidney Tubules, Male, Mice, Middle Aged, Polymorphism, Single Nucleotide, Transcriptome, Up-Regulation
Show Abstract · Added January 3, 2019
In this trans-ethnic multi-omic study, we reinterpret the genetic architecture of blood pressure to identify genes, tissues, phenomes and medication contexts of blood pressure homeostasis. We discovered 208 novel common blood pressure SNPs and 53 rare variants in genome-wide association studies of systolic, diastolic and pulse pressure in up to 776,078 participants from the Million Veteran Program (MVP) and collaborating studies, with analysis of the blood pressure clinical phenome in MVP. Our transcriptome-wide association study detected 4,043 blood pressure associations with genetically predicted gene expression of 840 genes in 45 tissues, and mouse renal single-cell RNA sequencing identified upregulated blood pressure genes in kidney tubule cells.
0 Communities
1 Members
0 Resources
15 MeSH Terms
Bid maintains mitochondrial cristae structure and function and protects against cardiac disease in an integrative genomics study.
Salisbury-Ruf CT, Bertram CC, Vergeade A, Lark DS, Shi Q, Heberling ML, Fortune NL, Okoye GD, Jerome WG, Wells QS, Fessel J, Moslehi J, Chen H, Roberts LJ, Boutaud O, Gamazon ER, Zinkel SS
(2018) Elife 7:
MeSH Terms: Animals, Apoptosis, BH3 Interacting Domain Death Agonist Protein, Beclin-1, Cell Respiration, Fibrosis, Gene Expression Regulation, Genome-Wide Association Study, Genomics, Heart Diseases, Heart Ventricles, Humans, Mice, Inbred C57BL, Mitochondria, Mitochondrial Proton-Translocating ATPases, Mutation, Myeloid Progenitor Cells, Myocardial Infarction, Myocytes, Cardiac, Polymorphism, Single Nucleotide, Protein Multimerization, Protein Structure, Secondary, Protein Subunits, Reactive Oxygen Species, Reproducibility of Results, Up-Regulation
Show Abstract · Added December 11, 2018
Bcl-2 family proteins reorganize mitochondrial membranes during apoptosis, to form pores and rearrange cristae. In vitro and in vivo analysis integrated with human genetics reveals a novel homeostatic mitochondrial function for Bcl-2 family protein Bid. Loss of full-length Bid results in apoptosis-independent, irregular cristae with decreased respiration. mice display stress-induced myocardial dysfunction and damage. A gene-based approach applied to a biobank, validated in two independent GWAS studies, reveals that decreased genetically determined BID expression associates with myocardial infarction (MI) susceptibility. Patients in the bottom 5% of the expression distribution exhibit >4 fold increased MI risk. Carrier status with nonsynonymous variation in Bid's membrane binding domain, Bid, associates with MI predisposition. Furthermore, Bid but not Bid associates with Mcl-1, previously implicated in cristae stability; decreased MCL-1 expression associates with MI. Our results identify a role for Bid in homeostatic mitochondrial cristae reorganization, that we link to human cardiac disease.
© 2018, Salisbury-Ruf et al.
0 Communities
3 Members
0 Resources
26 MeSH Terms
A Type 1 Diabetes Genetic Risk Score Predicts Progression of Islet Autoimmunity and Development of Type 1 Diabetes in Individuals at Risk.
Redondo MJ, Geyer S, Steck AK, Sharp S, Wentworth JM, Weedon MN, Antinozzi P, Sosenko J, Atkinson M, Pugliese A, Oram RA, Type 1 Diabetes TrialNet Study Group
(2018) Diabetes Care 41: 1887-1894
MeSH Terms: Adolescent, Adult, Autoantibodies, Autoimmunity, Child, Child, Preschool, Diabetes Complications, Diabetes Mellitus, Type 1, Disease Progression, Female, Genetic Predisposition to Disease, Genotype, HLA-DQ Antigens, Humans, Infant, Islets of Langerhans, Male, Middle Aged, Polymorphism, Single Nucleotide, Prognosis, Risk Factors, Young Adult
Show Abstract · Added July 23, 2018
OBJECTIVE - We tested the ability of a type 1 diabetes (T1D) genetic risk score (GRS) to predict progression of islet autoimmunity and T1D in at-risk individuals.
RESEARCH DESIGN AND METHODS - We studied the 1,244 TrialNet Pathway to Prevention study participants (T1D patients' relatives without diabetes and with one or more positive autoantibodies) who were genotyped with Illumina ImmunoChip (median [range] age at initial autoantibody determination 11.1 years [1.2-51.8], 48% male, 80.5% non-Hispanic white, median follow-up 5.4 years). Of 291 participants with a single positive autoantibody at screening, 157 converted to multiple autoantibody positivity and 55 developed diabetes. Of 953 participants with multiple positive autoantibodies at screening, 419 developed diabetes. We calculated the T1D GRS from 30 T1D-associated single nucleotide polymorphisms. We used multivariable Cox regression models, time-dependent receiver operating characteristic curves, and area under the curve (AUC) measures to evaluate prognostic utility of T1D GRS, age, sex, Diabetes Prevention Trial-Type 1 (DPT-1) Risk Score, positive autoantibody number or type, HLA DR3/DR4-DQ8 status, and race/ethnicity. We used recursive partitioning analyses to identify cut points in continuous variables.
RESULTS - Higher T1D GRS significantly increased the rate of progression to T1D adjusting for DPT-1 Risk Score, age, number of positive autoantibodies, sex, and ethnicity (hazard ratio [HR] 1.29 for a 0.05 increase, 95% CI 1.06-1.6; = 0.011). Progression to T1D was best predicted by a combined model with GRS, number of positive autoantibodies, DPT-1 Risk Score, and age (7-year time-integrated AUC = 0.79, 5-year AUC = 0.73). Higher GRS was significantly associated with increased progression rate from single to multiple positive autoantibodies after adjusting for age, autoantibody type, ethnicity, and sex (HR 2.27 for GRS >0.295, 95% CI 1.47-3.51; = 0.0002).
CONCLUSIONS - The T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.
© 2018 by the American Diabetes Association.
0 Communities
1 Members
0 Resources
22 MeSH Terms
Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation.
Gamazon ER, Segrè AV, van de Bunt M, Wen X, Xi HS, Hormozdiari F, Ongen H, Konkashbaev A, Derks EM, Aguet F, Quan J, GTEx Consortium, Nicolae DL, Eskin E, Kellis M, Getz G, McCarthy MI, Dermitzakis ET, Cox NJ, Ardlie KG
(2018) Nat Genet 50: 956-967
MeSH Terms: Disease, Gene Expression, Gene Expression Profiling, Gene Expression Regulation, Genome-Wide Association Study, Genotype, Humans, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Quantitative Trait, Heritable
Show Abstract · Added July 1, 2018
We apply integrative approaches to expression quantitative loci (eQTLs) from 44 tissues from the Genotype-Tissue Expression project and genome-wide association study data. About 60% of known trait-associated loci are in linkage disequilibrium with a cis-eQTL, over half of which were not found in previous large-scale whole blood studies. Applying polygenic analyses to metabolic, cardiovascular, anthropometric, autoimmune, and neurodegenerative traits, we find that eQTLs are significantly enriched for trait associations in relevant pathogenic tissues and explain a substantial proportion of the heritability (40-80%). For most traits, tissue-shared eQTLs underlie a greater proportion of trait associations, although tissue-specific eQTLs have a greater contribution to some traits, such as blood pressure. By integrating information from biological pathways with eQTL target genes and applying a gene-based approach, we validate previously implicated causal genes and pathways, and propose new variant and gene associations for several complex traits, which we replicate in the UK BioBank and BioVU.
0 Communities
1 Members
0 Resources
11 MeSH Terms
Exploring the role of low-frequency and rare exonic variants in alcohol and tobacco use.
Marees AT, Hammerschlag AR, Bastarache L, de Kluiver H, Vorspan F, van den Brink W, Smit DJ, Denys D, Gamazon ER, Li-Gao R, Breetvelt EJ, de Groot MCH, Galesloot TE, Vermeulen SH, Poppelaars JL, Souverein PC, Keeman R, de Mutsert R, Noordam R, Rosendaal FR, Stringa N, Mook-Kanamori DO, Vaartjes I, Kiemeney LA, den Heijer M, van Schoor NM, Klungel OH, Maitland-Van der Zee AH, Schmidt MK, Polderman TJC, van der Leij AR, Posthuma D, Derks EM
(2018) Drug Alcohol Depend 188: 94-101
MeSH Terms: Alcohol Drinking, Alcoholism, Cohort Studies, Exons, Female, Genetic Predisposition to Disease, Genetic Variation, Humans, Male, Nerve Tissue Proteins, Polymorphism, Single Nucleotide, Receptors, Nicotinic, Risk Factors, Tobacco Use, Tobacco Use Disorder
Show Abstract · Added May 26, 2018
BACKGROUND - Alcohol and tobacco use are heritable phenotypes. However, only a small number of common genetic variants have been identified, and common variants account for a modest proportion of the heritability. Therefore, this study aims to investigate the role of low-frequency and rare variants in alcohol and tobacco use.
METHODS - We meta-analyzed ExomeChip association results from eight discovery cohorts and included 12,466 subjects and 7432 smokers in the analysis of alcohol consumption and tobacco use, respectively. The ExomeChip interrogates low-frequency and rare exonic variants, and in addition a small pool of common variants. We investigated top variants in an independent sample in which ICD-9 diagnoses of "alcoholism" (N = 25,508) and "tobacco use disorder" (N = 27,068) had been assessed. In addition to the single variant analysis, we performed gene-based, polygenic risk score (PRS), and pathway analyses.
RESULTS - The meta-analysis did not yield exome-wide significant results. When we jointly analyzed our top results with the independent sample, no low-frequency or rare variants reached significance for alcohol consumption or tobacco use. However, two common variants that were present on the ExomeChip, rs16969968 (p = 2.39 × 10) and rs8034191 (p = 6.31 × 10) located in CHRNA5 and AGPHD1 at 15q25.1, showed evidence for association with tobacco use.
DISCUSSION - Low-frequency and rare exonic variants with large effects do not play a major role in alcohol and tobacco use, nor does the aggregate effect of ExomeChip variants. However, our results confirmed the role of the CHRNA5-CHRNA3-CHRNB4 cluster of nicotinic acetylcholine receptor subunit genes in tobacco use.
Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.
0 Communities
1 Members
0 Resources
15 MeSH Terms
CD36 Modulates Fasting and Preabsorptive Hormone and Bile Acid Levels.
Shibao CA, Celedonio JE, Tamboli R, Sidani R, Love-Gregory L, Pietka T, Xiong Y, Wei Y, Abumrad NN, Abumrad NA, Flynn CR
(2018) J Clin Endocrinol Metab 103: 1856-1866
MeSH Terms: Adult, African Americans, Bile Acids and Salts, CD36 Antigens, Case-Control Studies, Energy Metabolism, Fasting, Female, Genotype, Hormones, Humans, Intestinal Absorption, Middle Aged, Polymorphism, Single Nucleotide
Show Abstract · Added May 14, 2018
Context - Abnormal fatty acid (FA) metabolism contributes to diabetes and cardiovascular disease. The FA receptor CD36 has been linked to risk of metabolic syndrome. In rodents CD36 regulates various aspects of fat metabolism, but whether it has similar actions in humans is unknown. We examined the impact of a coding single-nucleotide polymorphism in CD36 on postprandial hormone and bile acid (BA) responses.
Objective - To examine whether the minor allele (G) of coding CD36 variant rs3211938 (G/T), which reduces CD36 level by ∼50%, influences hormonal responses to a high-fat meal (HFM).
Design - Obese African American (AA) women carriers of the G allele of rs3211938 (G/T) and weight-matched noncarriers (T/T) were studied before and after a HFM.
Setting - Two-center study.
Participants - Obese AA women.
Intervention - HFM.
Main Outcome Measures - Early preabsorptive responses (10 minutes) and extended excursions in plasma hormones [C-peptide, insulin, incretins, ghrelin fibroblast growth factor (FGF)19, FGF21], BAs, and serum lipoproteins (chylomicrons, very-low-density lipoprotein) were determined.
Results - At fasting, G-allele carriers had significantly reduced cholesterol and glycodeoxycholic acid and consistent but nonsignificant reductions of serum lipoproteins. Levels of GLP-1 and pancreatic polypeptide (PP) were reduced 60% to 70% and those of total BAs were 1.8-fold higher. After the meal, G-allele carriers displayed attenuated early (-10 to 10 minute) responses in insulin, C-peptide, GLP-1, gastric inhibitory peptide, and PP. BAs exhibited divergent trends in G allele carriers vs noncarriers concomitant with differential FGF19 responses.
Conclusions - CD36 plays an important role in the preabsorptive hormone and BA responses that coordinate brain and gut regulation of energy metabolism.
0 Communities
1 Members
0 Resources
14 MeSH Terms
A Large-Scale Multi-ancestry Genome-wide Study Accounting for Smoking Behavior Identifies Multiple Significant Loci for Blood Pressure.
Sung YJ, Winkler TW, de Las Fuentes L, Bentley AR, Brown MR, Kraja AT, Schwander K, Ntalla I, Guo X, Franceschini N, Lu Y, Cheng CY, Sim X, Vojinovic D, Marten J, Musani SK, Li C, Feitosa MF, Kilpeläinen TO, Richard MA, Noordam R, Aslibekyan S, Aschard H, Bartz TM, Dorajoo R, Liu Y, Manning AK, Rankinen T, Smith AV, Tajuddin SM, Tayo BO, Warren HR, Zhao W, Zhou Y, Matoba N, Sofer T, Alver M, Amini M, Boissel M, Chai JF, Chen X, Divers J, Gandin I, Gao C, Giulianini F, Goel A, Harris SE, Hartwig FP, Horimoto ARVR, Hsu FC, Jackson AU, Kähönen M, Kasturiratne A, Kühnel B, Leander K, Lee WJ, Lin KH, 'an Luan J, McKenzie CA, Meian H, Nelson CP, Rauramaa R, Schupf N, Scott RA, Sheu WHH, Stančáková A, Takeuchi F, van der Most PJ, Varga TV, Wang H, Wang Y, Ware EB, Weiss S, Wen W, Yanek LR, Zhang W, Zhao JH, Afaq S, Alfred T, Amin N, Arking D, Aung T, Barr RG, Bielak LF, Boerwinkle E, Bottinger EP, Braund PS, Brody JA, Broeckel U, Cabrera CP, Cade B, Caizheng Y, Campbell A, Canouil M, Chakravarti A, CHARGE Neurology Working Group, Chauhan G, Christensen K, Cocca M, COGENT-Kidney Consortium, Collins FS, Connell JM, de Mutsert R, de Silva HJ, Debette S, Dörr M, Duan Q, Eaton CB, Ehret G, Evangelou E, Faul JD, Fisher VA, Forouhi NG, Franco OH, Friedlander Y, Gao H, GIANT Consortium, Gigante B, Graff M, Gu CC, Gu D, Gupta P, Hagenaars SP, Harris TB, He J, Heikkinen S, Heng CK, Hirata M, Hofman A, Howard BV, Hunt S, Irvin MR, Jia Y, Joehanes R, Justice AE, Katsuya T, Kaufman J, Kerrison ND, Khor CC, Koh WP, Koistinen HA, Komulainen P, Kooperberg C, Krieger JE, Kubo M, Kuusisto J, Langefeld CD, Langenberg C, Launer LJ, Lehne B, Lewis CE, Li Y, Lifelines Cohort Study, Lim SH, Lin S, Liu CT, Liu J, Liu J, Liu K, Liu Y, Loh M, Lohman KK, Long J, Louie T, Mägi R, Mahajan A, Meitinger T, Metspalu A, Milani L, Momozawa Y, Morris AP, Mosley TH, Munson P, Murray AD, Nalls MA, Nasri U, Norris JM, North K, Ogunniyi A, Padmanabhan S, Palmas WR, Palmer ND, Pankow JS, Pedersen NL, Peters A, Peyser PA, Polasek O, Raitakari OT, Renström F, Rice TK, Ridker PM, Robino A, Robinson JG, Rose LM, Rudan I, Sabanayagam C, Salako BL, Sandow K, Schmidt CO, Schreiner PJ, Scott WR, Seshadri S, Sever P, Sitlani CM, Smith JA, Snieder H, Starr JM, Strauch K, Tang H, Taylor KD, Teo YY, Tham YC, Uitterlinden AG, Waldenberger M, Wang L, Wang YX, Wei WB, Williams C, Wilson G, Wojczynski MK, Yao J, Yuan JM, Zonderman AB, Becker DM, Boehnke M, Bowden DW, Chambers JC, Chen YI, de Faire U, Deary IJ, Esko T, Farrall M, Forrester T, Franks PW, Freedman BI, Froguel P, Gasparini P, Gieger C, Horta BL, Hung YJ, Jonas JB, Kato N, Kooner JS, Laakso M, Lehtimäki T, Liang KW, Magnusson PKE, Newman AB, Oldehinkel AJ, Pereira AC, Redline S, Rettig R, Samani NJ, Scott J, Shu XO, van der Harst P, Wagenknecht LE, Wareham NJ, Watkins H, Weir DR, Wickremasinghe AR, Wu T, Zheng W, Kamatani Y, Laurie CC, Bouchard C, Cooper RS, Evans MK, Gudnason V, Kardia SLR, Kritchevsky SB, Levy D, O'Connell JR, Psaty BM, van Dam RM, Sims M, Arnett DK, Mook-Kanamori DO, Kelly TN, Fox ER, Hayward C, Fornage M, Rotimi CN, Province MA, van Duijn CM, Tai ES, Wong TY, Loos RJF, Reiner AP, Rotter JI, Zhu X, Bierut LJ, Gauderman WJ, Caulfield MJ, Elliott P, Rice K, Munroe PB, Morrison AC, Cupples LA, Rao DC, Chasman DI
(2018) Am J Hum Genet 102: 375-400
MeSH Terms: Blood Pressure, Cohort Studies, Continental Population Groups, Diastole, Epistasis, Genetic, Female, Genetic Loci, Genome-Wide Association Study, Humans, Male, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Reproducibility of Results, Smoking, Systole
Show Abstract · Added April 10, 2018
Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactions in 610,091 individuals. Stage 1 analysis examined ∼18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-up analysis of promising variants in 480,178 additional individuals from five ancestries. We identified 15 loci that were genome-wide significant (p < 5 × 10) in stage 1 and formally replicated in stage 2. A combined stage 1 and 2 meta-analysis identified 66 additional genome-wide significant loci (13, 35, and 18 loci in European, African, and trans-ancestry, respectively). A total of 56 known BP loci were also identified by our results (p < 5 × 10). Of the newly identified loci, ten showed significant interaction with smoking status, but none of them were replicated in stage 2. Several loci were identified in African ancestry, highlighting the importance of genetic studies in diverse populations. The identified loci show strong evidence for regulatory features and support shared pathophysiology with cardiometabolic and addiction traits. They also highlight a role in BP regulation for biological candidates such as modulators of vascular structure and function (CDKN1B, BCAR1-CFDP1, PXDN, EEA1), ciliopathies (SDCCAG8, RPGRIP1L), telomere maintenance (TNKS, PINX1, AKTIP), and central dopaminergic signaling (MSRA, EBF2).
Copyright © 2018 American Society of Human Genetics. All rights reserved.
0 Communities
1 Members
0 Resources
15 MeSH Terms